cleaning-data

What problem does it solve?

This Skill provides a structured approach to data quality remediation in DataPeeker sessions, enabling automatic detection and systematic remediation of duplicates, outliers, NULL handling, and free-text categorization to produce analysis-ready datasets.

Core Features & Use Cases

• Automated data cleaning pipeline: detect and remediate duplicates, outliers, and inconsistent categories, while tracking rationale.
• Phase-guided workflow: supports 5-phase process (scope, detection, strategy, execution, verification) with audit trail.
• Deployable in analysis sessions: integrates with importing-data and data-analytics workflows for reproducible results.
• Use Case: Before a guided-investigation or exploratory-analysis workflow, run cleaning-data to generate clean tables and quality reports ready for analysis.

Quick Start

Execute the cleaning pipeline on the current dataset to produce clean_[table] tables for downstream analyses.